Using AI to predict outcomes of liver transplants from deceased donors
Official title: Generative-AI based system for accurate prediction of deceased donor liver-transplant (DDLT) outcome and viability
Funded at University of Louisville · NIH-10654166 · Data updated 2026-11-12
This study is working on a smart computer program that helps doctors look at liver tissues from donors who have passed away, making it easier to decide if the liver is good for transplant, which can lead to better outcomes and less chance of rejection.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Louisville NIH-funded |
| Lab location | 1 site (Louisville, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $464,073 |
| NIH project number | 1R15DK136105-01 |
| Project ID | NIH-10654166 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients with chronic liver disease, acute liver failure, or liver cancer who may require a deceased donor liver transplant.
✕Less likely to help
Patients who are not candidates for liver transplantation or those with non-liver-related conditions may not benefit from this research.
What this research studies
This research aims to develop a Generative AI-based system that assists pathologists and surgeons in evaluating liver tissues from deceased donors during transplantation. The system will create virtual slides to enhance the histopathological evaluation process, which is crucial for determining the viability of the liver for transplant. By utilizing advanced AI techniques, the project seeks to improve the accuracy of predicting transplant outcomes and reduce the risk of rejecting viable livers. This innovative approach addresses the time-sensitive nature of liver transplantation and aims to optimize the use of available organs.
Why it matters
Potential benefit: If successful, this research could significantly improve the outcomes of liver transplants by ensuring that more viable organs are successfully utilized.
How similar studies have gone: While the use of AI in medical evaluations is gaining traction, this specific application in liver transplantation is relatively novel and has not been extensively tested in prior studies.
Where this research is happening
-
University of Louisville
Louisville, United States Active
Researchers
- Principal investigator: El-Baz, Ayman S — University of Louisville
- Study coordinator: El-Baz, Ayman S
Related research
Other NIH-funded projects on the same topics.
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Innate and Adaptive Immunoregulation in Liver Transplant Ischemia/Reperfusion Injury
A liver transplant-focused project investigates how the body’s early and later immune responses affect transplant failure or success after reperfusion.
-
Biomarker-guided reduction of immunosuppression after liver transplant
A multicenter trial tests whether using blood biomarkers to guide tapering of immunosuppression in adults with liver transplants can keep people healthy while lowering drug exposure.
-
Understanding how liver disease progresses in people waiting for a liver transplant
A study will build a knowledge base that maps how liver disease worsens over time in patients on the transplant waitlist to improve care and planning.
-
Biomarkers to tailor liver transplant immunosuppression
A multi-center study using blood biomarkers to guide how immunosuppressive drugs are given to liver transplant patients.
-
Pre-Transplant Immune Health Marker Panel to Predict Liver Transplant Outcomes
A patient-facing biomarker test used before liver transplant to help doctors estimate the risk of early death after the transplant.
-
Thinking skills and self-care after a liver transplant
This project follows people who received liver transplants to learn how changes in thinking and memory affect managing medicines, clinic visits, and overall health.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.